SOTAVerified

Time Series Analysis

Time Series Analysis is a statistical technique used to analyze and model time-based data. It is used in various fields such as finance, economics, and engineering to analyze patterns and trends in data over time. The goal of time series analysis is to identify the underlying patterns, trends, and seasonality in the data, and to use this information to make informed predictions about future values.

( Image credit: Autoregressive CNNs for Asynchronous Time Series )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5226–5250 of 6748 papers

TitleStatusHype
On the Success Rate of Crossover Operators for Genetic Programming with Offspring Selection—0
On the suitability of generalized regression neural networks for GNSS position time series prediction for geodetic applications in geodesy and geophysics—0
On the Susceptibility and Robustness of Time Series Models through Adversarial Attack and Defense—0
On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis—0
On the Time Series Length for an Accurate Fractal Analysis in Network Systems—0
A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0—0
On the Usage of Generative Models for Network Anomaly Detection in Multivariate Time-Series—0
On the Use of Dimension Reduction or Signal Separation Methods for Nitrogen River Pollution Source Identification—0
On the use of generative deep neural networks to synthesize artificial multichannel EEG signals—0
On the use of Singular Spectrum Analysis—0
On the Use of Time Series Kernel and Dimensionality Reduction to Identify the Acquisition of Antimicrobial Multidrug Resistance in the Intensive Care Unit—0
On the variability of functional connectivity and network measures in source-reconstructed EEG time-series—0
Operator Autoencoders: Learning Physical Operations on Encoded Molecular Graphs—0
OPP-Miner: Order-preserving sequential pattern mining—0
Optimal Attack against Autoregressive Models by Manipulating the Environment—0
Optimal change point detection in Gaussian processes—0
Optimal Combination Forecasts on Retail Multi-Dimensional Sales Data—0
Optimal Copula Transport for Clustering Multivariate Time Series—0
Optimal Event Monitoring through Internet Mashup over Multivariate Time Series—0
Optimal Latent Space Forecasting for Large Collections of Short Time Series Using Temporal Matrix Factorization—0
Optimally adaptive Bayesian spectral density estimation for stationary and nonstationary processes—0
Optimally fuzzy temporal memory—0
Optimal model-free prediction from multivariate time series—0
Optimal Policies for Observing Time Series and Related Restless Bandit Problems—0
Optimal Prediction Intervals for Macroeconomic Time Series Using Chaos and NSGA II—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47—Unverified
2GRU-D - APC (n = 1)F127.3—Unverified
3GRU-APC (n = 1)F125.7—Unverified
4GRU-DF122.5—Unverified
5GRUF122.3—Unverified
6GRU-SimpleF122.2—Unverified
7GRU-MeanF122.1—Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51—Unverified
2ViT% Test Accuracy98.11—Unverified
3FlexTCN-4% Test Accuracy97.73—Unverified
4MatchboxNet% Test Accuracy97.4—Unverified
5CKCNN (100k)% Test Accuracy95.27—Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73—Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13—Unverified